Author name cluster

Decai Li

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6 papers

2 author rows

EAAI Journal 2025 Journal Article

Parameters identification of magnetorheological damper based on particle swarm optimization algorithm

Qianqian Guo
Xiaolong Yang
Kangjun Li
Decai Li

The parameter setting of the optimization algorithm is of significant importance in establishing a mechanical model with high accuracy. This study employs a combination of experimental and numerical methods to comprehensively examine the impact of optimization algorithm parameters on the accuracy of fitting results. The objective is to provide technical support for the precise prediction of the damping force in the control of the suspension system, as well as the optimization of vehicle driving performance. This paper employs the most prevalent particle swarm optimization algorithm and meticulously examines the impact of alterations in parameters, including the number of particles, the number of iterations and the learning factors, on the identification outcomes. The experimental data pertaining to the magnetorheological damper is obtained through investigation, and the parameters of the magnetorheological damper are identified through the utilisation of a numerical research methodology, specifically the particle swarm optimization algorithm. Finally, the veracity of the identified results is validated through a comparison of the identified damping force with the experimental damping force, thereby illustrating the significance of optimizing the algorithm parameter settings in enhancing the precision of the mechanical model.

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IROS Conference 2025 Conference Paper

Real-Time Optimization-Based Quadrotor Trajectory Generation with Kinodynamic Constraints in Unknown Environments

Pinhui Zhao
Decai Li
Minjiang Wu
Yuyang Zhou
Yuqing He

Indoor disaster relief and rescue missions require quadrotors to fully exploit their maneuverability in real-time. However, the computational complexity induced by the underactuated kinodynamics conflicts with the rapid replanning requirement. For agile trajectory planning in cluttered and unknown environments, we propose a real-time optimization-based quadrotor trajectory generation method that integrates kinodynamic constraints in both trajectory search and trajectory optimization phases to fully exploit maneuverability. To further improve efficiency, we introduce a waypoints selection strategy to reduce the computational burden of kinodynamic trajectory optimization by transforming obstacle avoidance constraints into waypoint constraints, thereby enabling safe trajectory optimization in real-time. Specifically, kinodynamic trajectories are searched under kinodynamic constraints, providing reliable initial values for subsequent numerical optimization. Nextly, a waypoints selection algorithm, based on an estimation of trajectory variation during optimization, is introduced to preserve the obstacle-avoidance properties obtained during the search phase by limiting the variation with waypoint constraints. Finally, trajectory is segmented by waypoints with fixed time intervals each segment and then optimized under kinodynamic constraints, ensuring real-time optimization at the cost of time allocation optimality. We evaluated our method through simulation and experimentally validate its performance in cluttered and unknown environments. The competence of proposed method is also validated in real-world experiments.

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AAAI Conference 2022 Conference Paper

Random Mapping Method for Large-Scale Terrain Modeling

Xu Liu
Decai Li
Yuqing He

The vast amount of data captured by robots in large-scale environments brings the computing and storage bottlenecks to the typical methods of modeling the spaces the robots travel in. In order to efficiently construct a compact terrain model from uncertain, incomplete point cloud data of large-scale environments, in this paper, we first propose a novel feature mapping method, named random mapping, based on the fast random construction of base functions, which can efficiently project the messy points in the low-dimensional space into the high-dimensional space where the points are approximately linearly distributed. Then, in this mapped space, we propose to learn a continuous linear regression model to represent the terrain. We show that this method can model the environments in much less computation time, memory consumption, and access time, with high accuracy. Furthermore, the models possess the generalization capabilities comparable to the performances on the training set, and its inference accuracy gradually increases as the random mapping dimension increases. To better solve the large-scale environmental modeling problem, we adopt the idea of parallel computing to train the models. This strategy greatly reduces the wall-clock time of calculation without losing much accuracy. Experiments show the effectiveness of the random mapping method and the effects of some important parameters on its performance. Moreover, we evaluate the proposed terrain modeling method based on the random mapping method and compare its performances with popular typical methods and state-of-art methods.

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IROS Conference 2021 Conference Paper

An Efficient and Continuous Representation for Occupancy Mapping with Random Mapping

Xu Liu 0026
Decai Li
Yuqing He

Generating meaningful spatial models of physical environments is a crucial ability for autonomous navigation of mobile robots. This paper considers the problem of building continuous occupancy maps from sparse and noisy sensor data. To this end, we propose a new method named random mapping maps that advances the popular methods in two aspects. Firstly, it can represent environment models in a memory-saving and time-saving manner by randomly mapping a low-dimensional feature space to a high-dimensional one where a linear model is learnt. Secondly, it can rapidly obtain accurate inferences of the occupancy states of the spatial locations. This technique is based on the random mapping that projects the measurement data into a random feature space in which a discriminative model is learnt by the available data. It can asymptotically represent the complexity of the real world as the mapping dimension increases. Evaluations of the proposed method were conducted on various environments to verify its availability to environment modeling. Its performances in terms of time and memory consumptions were evaluated quantitatively. Finally, as a practical application, experiments about path planning were conducted based on the gradients of the proposed representation of environment model.

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ICRA Conference 2021 Conference Paper

Multiresolution Representations for Large-Scale Terrain with Local Gaussian Process Regression

Xu Liu 0026
Decai Li
Yuqing He

To address the problem of building accurate and coherent models for large-scale terrains from incomplete and noisy sensor data, this paper proposes a novel framework that can efficiently infer terrain structures by divisionally providing the best linear unbiased estimates for the elevation values. To avoid data ambiguity caused by the uncertainty of sensor data, the proposed method introduces elevation filtering to extract the terrain surfaces, which reduces the amount of data greatly while the contained terrain information is basically unchanged. Then, for the large-scale terrains, the Gaussian mixture model is used to divide the interested regions, which remarkably improves the prediction accuracy and speed. Finally, for each subregion, a gaussian process regression model based on the static kernel is used to create a multiresolution terrain representation, which can deal with incompleteness of sensor data by considering the spatial correlations of the terrain. Evaluations of the proposed technique were conducted on diverse large-scale field terrains, including the quarry, planetary emulation terrain and highland, showing that the proposed method outperforms the state-of-art terrain modeling techniques in terms of the prediction accuracy, computation speed and memory consumption. As a practical application, the path planning problem was explored based on this terrain modeling technique to produce a better path.

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IROS Conference 2021 Conference Paper

Simultaneous Prediction of Pedestrian Trajectory and Actions based on Context Information Iterative Reasoning

Bo Chen
Decai Li
Yuqing He

Pedestrian trajectories and actions prediction in complex environment is challenging due to the complexity of human behavior and a variety of internal and external stimuli. Much works has gone towards predicting trajectories and actions separately without mining the coupling relationships between them, which is an important information for our humans to reason and predict. Inspired by this, we propose an end-to-end joint context information iterative reasoning network (CIR-Net). Specifically, a novel heterogeneous spatiotemporal graph module (HST-Graph) is proposed to encode and aggregate multiple types of context information of the motion pattern and the scene. And an action-trajectory hybrid guidance module is proposed to enhance the ability of long-time prediction by utilizing the internal coupling between actions and trajectory. Moreover, an iterative reasoning structure is designed to iteratively correcting the trajectory and actions prediction error. Experimental results on the ETH&UCY and VIRAT datasets demonstrate the favorable performance of the framework.

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